code: def fit_and_score(*args, **kwargs) -> dict:
      """Wrapper for sklearn's _fit_and_score function.
  
      Wrap the function sklearn.model_selection._validation._fit_and_score
      to, in turn, path sklearn's _score function to accept pipelines that
      drop samples during transforming, within a joblib parallel context.
  
      """
  
      def wrapper(*args, **kwargs) -> dict:
          with patch(
              "sklearn.model_selection._validation._score",
              score(_score),
          ):
              return _fit_and_score(*args, **kwargs)
  
      return wrapper(*args, **kwargs)
  
file_path: pycaret/pycaret/internal/patches/sklearn.py
imports: from typing import Any, Callable
  from unittest.mock import patch
  import numpy
  from sklearn.model_selection._validation import _fit_and_score, _score
  from sklearn.utils import check_random_state
  from pycaret.internal.pipeline import pipeline_predict_inverse_only
label_desc: Use image node_id 3 for calling a global function with example usage: fit_and_score() and returns: wrapper, _fit_and_score